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    <title>SBERT.net Models</title>
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    <style>
        .fa-active {
            color: #337ab7;
        }


        .header-cell {
            cursor: pointer;
        }

        .models-table thead th {
            position: sticky;
            top: 0;
            z-index: 1;
            background-color: #ffffff;
        }


        .info-icon {
            color: #007bff;
        }

        .info-icon:hover {
            color: #0056b3;
        }

        .info-icon-model {
            padding-left: 10px;
        }

        .bs-popover-auto[x-placement^=bottom], .bs-popover-bottom {
            margin-top: .5rem;
        }

        .popover {
            max-width: 400px;
        }
    </style>
</head>
<body>

<div id="app">
    <table class="table table-bordered table-sm">
        <thead>
            <tr>
                <th class="header-cell" @click="sortAsc = (sortBy=='name') ? sortAsc = !sortAsc : false; sortBy='name'">
                    <i class="fas fa-active" v-if="sortBy == 'name'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
                    Model Name
                </th>
                <th class="header-cell text-center" @click="sortAsc = (sortBy=='stsb') ? sortAsc = !sortAsc : false; sortBy='stsb'">
                    <i class="fas fa-active" v-if="sortBy == 'stsb'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
                    STSb
                     <span class="info-icon" data-trigger="hover" data-toggle="popover" title="STSbenchmark" data-content="Spearman-rank correlation on the STSbenchmark test set. Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
                </th>
                <th class="header-cell text-center" @click="sortAsc = (sortBy=='dupq') ? sortAsc = !sortAsc : false; sortBy='dupq'">
                     <i class="fas fa-active" v-if="sortBy == 'dupq'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
                    DupQ
                    <span class="info-icon" data-trigger="hover" data-toggle="popover" title="Duplicate Questions" data-content="Combination of two datasets for duplicate questions detection:<br>Mean-Average-Precision on the Quora Duplicate Questions Semantic Search test set.<br>Average-Precision on the Sprint duplicate questions test set.<br> Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
                </th>
                <th class="header-cell text-center" @click="sortAsc = (sortBy=='TwitterP') ? sortAsc = !sortAsc : false; sortBy='TwitterP'">
                    <i class="fas fa-active" v-if="sortBy == 'TwitterP'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
                    TwitterP
                     <span class="info-icon" data-trigger="hover" data-toggle="popover" title="Twitter Paraphrases" data-content="A test to find tweets that are considered paraphrases. Combination of the SemEval2015 Tweet paraphrase test set and the Twitter-URL-Corpus test set. Performance is measured using Average Precision.<br> Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
                </th>
                <th class="header-cell text-center" @click="sortAsc = (sortBy=='scidocs') ? sortAsc = !sortAsc : false; sortBy='scidocs'">
                    <i class="fas fa-active" v-if="sortBy == 'scidocs'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
                    SciDocs
                     <span class="info-icon" data-trigger="hover" data-toggle="popover" title="SciDocs" data-content="A test to find similar scientific publications given a paper title. From SciDocs, we use the information which papers are often co-cited, co-read, or co-viewed. Performance is measured using MAP.<br> Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
                </th>
                <th class="header-cell text-center" @click="sortAsc = (sortBy=='clustering') ? sortAsc = !sortAsc : false; sortBy='clustering'">
                    <i class="fas fa-active" v-if="sortBy == 'clustering'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
                    Clustering
                     <span class="info-icon" data-trigger="hover" data-toggle="popover" title="Clustering" data-content="Here we test how well the embeddings can be used for clustering. We use three datasets: email subjects from 20NewsGroups, titles from 199 popular subreddits, questions from 121 StackExchanges. We cluster different sentence collections with sentences from 10-50 categories. Performance is measured using V-Measure. <br> Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
                </th>
                <th class="header-cell text-center" @click="sortAsc = (sortBy=='final_score') ? sortAsc = !sortAsc : false; sortBy='final_score'">
                    <i class="fas fa-active" v-if="sortBy == 'final_score'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
                    Avg. Performance
                     <span class="info-icon" data-trigger="hover" data-toggle="popover" title="Average Performance" data-content="Average Performance over all tasks.<br>Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>
                </th>
                <th class="header-cell text-center" @click="sortAsc = (sortBy=='speed') ? sortAsc = !sortAsc : false; sortBy='speed'">
                    <i class="fas fa-active" v-if="sortBy == 'speed'" v-bind:class="{ 'fa-sort-amount-up': !sortAsc, 'fa-sort-amount-down-alt': sortAsc }"></i>
                    Speed
                     <span class="info-icon" data-trigger="hover" data-toggle="popover" title="Encoding Speed" data-content="Encoding speed (sentences / sec) on a V100 GPU.<br>Higher = Better" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>

                </th>
            </tr>
        </thead>
        <tbody>
            <tr v-for="item in sortedModels">
                <td>
                    {{ item.name }}
                    <span class="info-icon info-icon-model" data-trigger="hover" data-toggle="popover" :title="item.name" :data-content="'<b>Base Model:</b> '+item.base_model+'<br><b>Pooling:</b> '+item.pooling+'<br><b>Training Data:</b> '+item.training_data+'<br><b>Dimensions:</b> '+item.dim+'<br><b>Size:</b> '+item.size+' MB'" data-html="true" data-placement="bottom"><i class="fas fa-info-circle"></i></span>

                </td>
                <td class="text-center">{{ item.stsb.toFixed(2) }}</td>
                <td class="text-center"><span :title="'Quora: '+item.qqp.toFixed(2)+' Sprint: '+item.sprint.toFixed(2)">{{ item.dupq.toFixed(2) }}</span></td>
                <td class="text-center">{{ item.TwitterP.toFixed(2) }}</td>
                <td class="text-center">{{ item.scidocs.toFixed(2) }}</td>
                <td class="text-center"><span :title="'Newsgroups: '+item.newsgroups.toFixed(2)+' Reddit: '+item.reddit.toFixed(2)+' StackExchange: '+item.stackexchange.toFixed(2)">{{ item.clustering.toFixed(2) }}</span></td>
                <td class="text-center">{{ item.final_score.toFixed(2) }}</td>
                <td class="text-center">{{ item.speed }}</td>
            </tr>
        </tbody>
    </table>
</div>

<script>
    var app = new Vue({
        el: '#app',
        data: {
            models: [
                {
                    "name": "stsb-mpnet-base-v2",
                    "base_model": "microsoft/mpnet-base",
                    "pooling": "Mean Pooling",
                    "training_data": "NLI+STSb",
                    "stsb": 88.57,
                    "qqp": 79.74,
                    "sprint": 90.34,
                    "TwitterP": 75.35,
                    "scidocs": 72.48,
                    "newsgroups": 31.56,
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                    "dim": 768
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                    "name": "stsb-roberta-base-v2",
                    "base_model": "roberta-base",
                    "pooling": "Mean Pooling",
                    "training_data": "NLI+STSb",
                    "stsb": 87.21,
                    "qqp": 78.68,
                    "sprint": 86.42,
                    "TwitterP": 73.44,
                    "scidocs": 69.83,
                    "newsgroups": 26.87,
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                    "name": "stsb-distilroberta-base-v2",
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                    "pooling": "Mean Pooling",
                    "training_data": "NLI+STSb",
                    "stsb": 86.41,
                    "qqp": 78.13,
                    "sprint": 87.28,
                    "TwitterP": 73.68,
                    "scidocs": 69.85,
                    "newsgroups": 28.63,
                    "reddit": 38.26,
                    "stackexchange": 46.16,
                    "speed": 4000,
                    "size": 292,
                    "dim": 768
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                    "name": "nli-mpnet-base-v2",
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                    "stsb": 86.53,
                    "qqp": 80.65,
                    "sprint": 85.79,
                    "TwitterP": 76.24,
                    "scidocs": 72.90,
                    "newsgroups": 36.56,
                    "reddit": 42.68,
                    "stackexchange": 50.90,
                    "speed": 2800,
                    "size": 385,
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                {
                    "name": "nli-roberta-base-v2",
                    "base_model": "roberta-base",
                    "pooling": "Mean Pooling",
                    "training_data": "NLI",
                    "stsb": 85.54,
                    "qqp": 78.73,
                    "sprint": 81.67,
                    "TwitterP": 74.28,
                    "scidocs": 69.86,
                    "newsgroups": 31.28,
                    "reddit": 39.58,
                    "stackexchange": 49.51,
                    "speed": 2300,
                    "size": 440,
                    "dim": 768
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                {
                    "name": "nli-distilroberta-base-v2",
                    "base_model": "distilroberta-base",
                    "pooling": "Mean Pooling",
                    "training_data": "NLI",
                    "stsb": 84.38,
                    "qqp": 78.47,
                    "sprint": 83.03,
                    "TwitterP": 73.86,
                    "scidocs": 70.23,
                    "newsgroups": 31.87,
                    "reddit": 39.12,
                    "stackexchange": 49.27,
                    "speed": 4000,
                    "size": 292,
                    "dim": 768
                },
                {
                    "name": "average_word_embeddings_glove.6B.300d",
                    "base_model": "Word Embeddings: GloVe",
                    "pooling": "Mean Pooling",
                    "training_data": "-",
                    "stsb": 61.77,
                    "qqp": 69.18,
                    "sprint": 86.96,
                    "TwitterP": 68.60,
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                    "newsgroups": 26.65,
                    "reddit": 28.37,
                    "stackexchange": 36.37,
                    "speed": 34000,
                    "size": 422,
                    "dim": 300
                },
                {
                    "name": "average_word_embeddings_komninos",
                    "base_model": "Word Embeddings: Komninos et al.",
                    "pooling": "Mean Pooling",
                    "training_data": "-",
                    "stsb": 61.56,
                    "qqp": 69.83,
                    "sprint": 85.55,
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                    "newsgroups": 27.53,
                    "reddit": 29.54,
                    "stackexchange": 39.35,
                    "speed": 22000,
                    "size": 237,
                    "dim": 300
                },
                /*{
                    "name": "average_word_embeddings_levy_dependency",
                    "base_model": "Word Embeddings: Levy et al.",
                    "pooling": "Mean Pooling",
                    "training_data": "-",
                    "stsb": 59.22,
                    "qqp": 64.62,
                    "sprint": 80.12,
                    "TwitterP": 70.79,
                    "scidocs": 60.04 ,
                    "newsgroups": 22.72,
                    "reddit": 24.23,
                    "stackexchange": 33.66,
                    "speed": 22000,
                    "size": 186,
                    "dim": 300
                },*/
                {
                    "name": "paraphrase-MiniLM-L12-v2",
                    "base_model": "microsoft/MiniLM-L12-H384-uncased",
                    "pooling": "Mean Pooling",
                    "training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
                    "stsb": 84.41,
                    "qqp":  84.64,
                    "sprint": 89.91,
                    "TwitterP": 75.34,
                    "scidocs": 80.08,
                    "newsgroups": 41.81,
                    "reddit": 44.42,
                    "stackexchange": 54.63,
                    "speed": 7500,
                    "size": 118,
                    "dim": 384
                },
                {
                    "name": "paraphrase-MiniLM-L6-v2",
                    "base_model": "nreimers/MiniLM-L6-H384-uncased",
                    "pooling": "Mean Pooling",
                    "training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
                    "stsb": 84.12,
                    "qqp":  84.25,
                    "sprint": 90.21,
                    "TwitterP": 76.32,
                    "scidocs": 78.91,
                    "newsgroups": 40.16,
                    "reddit": 42.71,
                    "stackexchange": 53.14,
                    "speed": 14200,
                    "size": 80,
                    "dim": 384
                },
                {
                    "name": "paraphrase-MiniLM-L3-v2",
                    "base_model": "nreimers/MiniLM-L3-H384-uncased",
                    "pooling": "Mean Pooling",
                    "training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
                    "stsb": 82.41,
                    "qqp":  83.29,
                    "sprint": 92.9,
                    "TwitterP": 76.14,
                    "scidocs": 77.71,
                    "newsgroups": 37.73,
                    "reddit": 41.18,
                    "stackexchange": 51.25,
                    "speed": 19000,
                    "size": 61,
                    "dim": 384
                },
                {
                    "name": "paraphrase-distilroberta-base-v2",
                    "base_model": "distilroberta-base",
                    "pooling": "Mean Pooling",
                    "training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
                    "stsb": 85.37,
                    "qqp": 84.31,
                    "sprint": 89.64,
                    "TwitterP": 73.96,
                    "scidocs":  80.25,
                    "newsgroups": 42.12,
                    "reddit": 47.53,
                    "stackexchange": 57.90,
                    "speed": 4000,
                    "size": 292,
                    "dim": 768
                },
                {
                    "name": "paraphrase-TinyBERT-L6-v2",
                    "base_model": "https://huggingface.co/nreimers/TinyBERT_L-6_H-768_v2",
                    "pooling": "Mean Pooling",
                    "training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
                    "stsb": 84.91,
                    "qqp": 84.23,
                    "sprint": 89.62,
                    "TwitterP": 75.39,
                    "scidocs":  81.51,
                    "newsgroups": 43.82,
                    "reddit": 44.61,
                    "stackexchange": 55.69,
                    "speed": 4500,
                    "size": 238,
                    "dim": 768
                },
                {
                    "name": "paraphrase-mpnet-base-v2",
                    "base_model": "microsoft/mpnet-base",
                    "pooling": "Mean Pooling",
                    "training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
                    "stsb": 86.99,
                    "qqp": 84.93,
                    "sprint": 90.67,
                    "TwitterP": 76.05,
                    "scidocs": 80.57,
                    "newsgroups": 48.13,
                    "reddit": 50.52,
                    "stackexchange": 59.79,
                    "speed": 2800,
                    "size": 387,
                    "dim": 768
                },
                /*{
                    "name": "paraphrase-albert-base-v2",
                    "base_model": "albert-base-v2",
                    "pooling": "Mean Pooling",
                    "training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
                    "stsb": 83.38,
                    "qqp": 83.28,
                    "sprint": 90.45,
                    "TwitterP": 74.83,
                    "scidocs": 77.83,
                    "newsgroups": 39.88,
                    "reddit": 42.64,
                    "stackexchange": 52.95,
                    "speed": 2400,
                    "size": 43,
                    "dim": 768
                },*/
                {
                    "name": "paraphrase-albert-small-v2",
                    "base_model": "nreimers/albert-small-v2",
                    "pooling": "Mean Pooling",
                    "training_data": "AllNLI, sentence-compression, SimpleWiki, altlex, msmarco-triplets, quora_duplicates, coco_captions,flickr30k_captions, yahoo_answers_title_question, S2ORC_citation_pairs, stackexchange_duplicate_questions, wiki-atomic-edits",
                    "stsb": 83.40,
                    "qqp": 83.54,
                    "sprint": 89.6,
                    "TwitterP": 74.51,
                    "scidocs": 80.28,
                    "newsgroups": 40.54,
                    "reddit": 41.54,
                    "stackexchange": 52.74,
                    "speed": 5000,
                    "size": 43,
                    "dim": 768
                },
                {
                    "name": "paraphrase-multilingual-mpnet-base-v2",
                    "base_model": "Teacher: paraphrase-mpnet-base-v2; Student: xlm-roberta-base",
                    "pooling": "Mean Pooling",
                    "training_data": "Multi-lingual model of paraphrase-mpnet-base-v2, extended to 50+ languages.",
                    "stsb": 86.82,
                    "qqp": 83.91,
                    "sprint": 91.1,
                    "TwitterP": 76.52,
                    "scidocs": 78.66,
                    "newsgroups": 44.65,
                    "reddit": 45.01,
                    "stackexchange": 52.73,
                    "speed": 2500,
                    "size": 969,
                    "dim": 768
                },
                {
                    "name": "paraphrase-multilingual-MiniLM-L12-v2",
                    "base_model": "Teacher: paraphrase-MiniLM-L12-v2; Student: microsoft/Multilingual-MiniLM-L12-H384",
                    "pooling": "Mean Pooling",
                    "training_data": "Multi-lingual model of paraphrase-multilingual-MiniLM-L12-v2, extended to 50+ languages.",
                    "stsb": 84.42,
                    "qqp": 83.89,
                    "sprint": 91.15,
                    "TwitterP": 74.94,
                    "scidocs": 78.27,
                    "newsgroups": 40.36,
                    "reddit": 41.49,
                    "stackexchange": 49.75,
                    "speed": 7500,
                    "size": 418,
                    "dim": 384
                },
                {
                    "name": "distiluse-base-multilingual-cased-v1",
                    "base_model": "Teacher: mUSE; Student: distilbert-base-multilingual",
                    "pooling": "Mean Pooling",
                    "training_data": "Multi-Lingual model of Universal Sentence Encoder for 15 languages: Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Portuguese, Russian, Spanish, Turkish.",
                    "stsb": 80.62,
                    "qqp": 81.10,
                    "sprint": 88.54,
                    "TwitterP": 76.24,
                    "scidocs": 70.41,
                    "newsgroups": 32.97,
                    "reddit": 42.93,
                    "stackexchange": 44.30,
                    "speed": 4000,
                    "size": 482,
                    "dim": 512
                },
                {
                    "name": "distiluse-base-multilingual-cased-v2",
                    "base_model": "Teacher: mUSE; Student: distilbert-base-multilingual",
                    "pooling": "Mean Pooling",
                    "training_data": "Multi-Lingual model of Universal Sentence Encoder for 50 languages.",
                    "stsb": 80.75,
                    "qqp": 79.89,
                    "sprint": 87.15,
                    "TwitterP": 76.26,
                    "scidocs": 70.39,
                    "newsgroups": 29.96,
                    "reddit": 39.95,
                    "stackexchange": 41.19,
                    "speed": 4000,
                    "size": 481,
                    "dim": 512
                }

            ],
            sortBy: 'final_score',
            sortAsc: false
        },
        methods: {

        },
        computed: {
            sortedModels: function() {
                //Add avg. for duplicate questions
                let models_ext = this.models.map(function(elem, index)  { elem.dupq = (elem.qqp + elem.sprint)/2.0; return elem;} );

                //Add avg. for clustering
                models_ext = models_ext.map(function(elem, index)  { elem.clustering = (elem.newsgroups + elem.reddit + elem.stackexchange)/3.0; return elem;} );

                //Final score
                models_ext = models_ext.map(function(elem, index)  { elem.final_score = (elem.stsb + elem.dupq + elem.TwitterP + elem.scidocs + elem.clustering)/ 5.0; return elem;} );

                return _.orderBy(models_ext, (item) => item[this.sortBy] || (this.sortAsc ? 9999 : -9999), this.sortAsc ? 'asc' : 'desc')
            }
        }
    })
</script>

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      $('[data-toggle="popover"]').popover()
    })
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